UMIC

This repository provides an unferenced image captioning metric from our ACL 2021 paper UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning.
Here, we provide the code to compute UMIC.

Usage (Updating the Descriptions)

Our code is based on UNITER. Therefore, please follow the install guideline for using Docker to load UNITER. In the next few weeks, we try to release the version without using the docker.

1. Install Prerequisites

We used the Docker image provided by the official repo of UNITER. Using the guideline in the repo, please install the docker.

2. Download the Visual Features

For image captioning task, COCO dataset is widely used. To download the visual features for coco captions, just download the image features for coco validation splits using the following command.

wget https://acvrpublicycchen.blob.core.windows.net/uniter/img_db/coco_val2014.tar

Please refer to the offical repo of UNITER for downloading other visual features.

3. Pre-processing the Textual Features (Captions)

The format of textual feature file(python dictionary, json format) is as follows:
'cands' : [list of candidate captions]
'img_fs' : [list of image file names]

4. Running the Script

Launching Docker

source launch_activate.sh $PATH_TO_STORAGE

Compute Score

python compute_score.py --data_type capeval1k \
                              --ckpt /storage/umic.pt \
                              --img_type \ coco_val2014 \

Reference

If you find this repo useful, please consider citing:

@inproceedings{lee-etal-2021-umic,
    title = "{UMIC}: An Unreferenced Metric for Image Captioning via Contrastive Learning",
    author = "Lee, Hwanhee  and
      Yoon, Seunghyun  and
      Dernoncourt, Franck  and
      Bui, Trung  and
      Jung, Kyomin",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-short.29",
    doi = "10.18653/v1/2021.acl-short.29",
    pages = "220--226",
}

Official PyTorch implementation of "Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning" (AAAI 2021)

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Comments

Missing and mismatching files

Hi Authors,

I greatly appreciate your work, establishing a new SOTA for referenced caption evaluation metric, and your contributions to open source. However, I noticed some issues in the current release.

It seems that some important files/directories for training the model and computing the score is not here by now, such as data/ and model/ as is in UNITER.

I also noticed some mismatches between README and the actual files, like launch_activate.sh, compute_score.py in README, while launch_container.sh and compute_metric.py in the current repo.

Any plan to fix them? Thanks.

opened by blmoistawinde 2
Access to the CapEval1k Dataset

Thanks for your excellent work! I'm working on caption evaluation and want to test other automatic metrics on your CapEval1k dataset, but there is no download link for the original dataset. I would appreciate it if you could make the dataset publicly available.

opened by PKUCSS 0